Methods and systems for emission computed tomography image reconstruction

ABSTRACT

The present disclosure relates to a method and system for reconstructing an Emission Computed Tomography (ECT) image based on locally adaptive gating. ECT projection data relating to a subject may be obtained. The ECT projection data may correspond to a plurality of voxels in a reconstructed image domain. The ECT projection data may be divided into a plurality of frames. A plurality of intermediate images may be reconstructed according to the plurality of frames. A plurality of motion amplitudes of the plurality of voxels may be obtained based on the plurality of intermediate images. A plurality of gate numbers may be determined for the plurality of voxels based on the plurality of motion amplitudes of the plurality of voxels. A plurality of ECT images may be reconstructed based on the ECT projection data and the plurality of gate numbers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 15/618,425, field on Jun. 9, 2017, which is acontinuation-in-part of U.S. patent application Ser. No. 15/386,048,entitled METHODS AND SYSTEMS FOR EMISSION COMPUTED TOMOGRAPHY IMAGERECONSTRUCTION, filed on Dec. 21, 2016, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image reconstruction, andmore specifically relates to methods and systems for reconstructing alocally adaptively gated image.

BACKGROUND

Emission Computed Tomography (ECT) has been widely used in medicine fordiagnosis and other purposes. A subject, such as a patient, may bescanned by a scanner to obtain medical images. When the medical imagingsystem is used for chest or upper abdomen examinations, respiratorymotion of the lungs and/or cardiac movements of the heart of a subjectmay lead to motion blur in the medical images. The motion blur may bereduced by using a gating approach.

A single field of view of a total-body PET scanner may cover the entirebody of a subject. Various regions of the entire body of the subject maymove to various degrees during the scanning of the subject. Aconventional gating approach may be inapplicable to the total-body PETscanner as it would divide the data of entire body into different framescovering different motion phases, while noticeable motions may occuronly in selected regions of the body such as the chest and the abdomen.It is desirable to develop methods and systems for reconstructing dataacquired by a total-body PET scanner with reduced motion blur withoutover-gating of a region of the subject that is not significantlyaffected by the motion of the subject during the scanning.

SUMMARY

A first aspect of the present disclosure relates to a method forreconstructing an Emission Computed Tomography (ECT) image. The methodmay be implemented on at least one machine each of which has at leastone processor and storage. The method may include one or more of thefollowing operations. ECT projection data relating to a subject may beobtained. The ECT projection data may correspond to a plurality ofvoxels in a reconstructed image domain. The ECT projection data may bedivided into a plurality of frames. A plurality of intermediate imagesmay be reconstructed according to the plurality of frames. A pluralityof motion amplitudes of the plurality of voxels may be obtained based onthe plurality of intermediate images. A plurality of gate numbers may bedetermined for the plurality of voxels based on the plurality of motionamplitudes of the plurality of voxels. A plurality of ECT images may bereconstructed based on the ECT projection data and the plurality of gatenumbers.

A second aspect of the present disclosure relates to a system forreconstructing an Emission Computed Tomography (ECT) image. The systemmay include an acquisition module and a processing module. Theacquisition module may obtain ECT projection data relating to a subject.The ECT projection data may correspond to a plurality of voxels in areconstructed image domain. The processing module may include a gatingcontrol unit and a reconstruction unit. The gating control unit maydivide the ECT projection data into a plurality of frames. Thereconstruction unit may reconstruct a plurality of intermediate imagesaccording to the plurality of frames. The gating control unit mayfurther determine a plurality of motion amplitudes of the plurality ofvoxels based on the plurality of intermediate images; and determining,based on the plurality of motion amplitudes of the plurality of voxels,a plurality of gate numbers for the plurality of voxels. Thereconstruction unit may further reconstruct a plurality of ECT imagesbased on the ECT projection data and the plurality of gate numbers.

In some embodiments, the ECT projection data may be acquired using asingle-bed whole-body PET scanner. The ECT projection data may beacquired in a whole field of view (FOV). The whole field of viewcomprises a plurality of local VOIs. The gate numbers corresponding toat least two of the local VOIs may be different. In some embodiments, agating area in the whole field of view may be determined based on theplurality of motion amplitudes of the plurality of voxels. The gatingarea may be determined based on a user input, or based on the motionamplitudes of the spatial points of the subject.

In some embodiments, the plurality of intermediate images may include afirst image frame and a second image frame. The plurality of motionamplitudes of the plurality of voxels may be determined based on thefirst image frame and the second image frame. In some embodiments, todetermine the plurality of motion amplitudes of the plurality of voxels,the first image frame and the second image frame may be registered toobtain a plurality of 3D motion vectors; and the plurality of motionamplitudes of the plurality of voxels may be determined based on theplurality of motion vectors.

In some embodiments, the registration of the first image frame and thesecond image frame may include one or more of the following operations.Two-dimensional image registration may be performed based on the firstimage frame and the second image frame. For each spatial point, a 2Dmotion vector in a coronal plane and a 2D motion vector in a sagittalplane may be determined based on the registration. The plurality of 3Dmotion vectors may be obtained based on the 2D motion vectors in acoronal plane and the 2D motion vectors in a sagittal plane. Theplurality of 3D motion vectors may form a 3D motion vector field.

In some embodiments, the registration of the first image frame and thesecond image frame may include performing 3D image registration. Basedon the 3D image registration, the plurality of 3D motion vectors may bedetermined. For instance, a 3D motion vector field including theplurality of 3D motion vectors may be determined based on the 3D imageregistration.

In some embodiments, the first image frame and the second image framemay correspond to a first involuntary motion phase and a secondinvoluntary motion phase of the subject, respectively. The involuntarymotion may be respiratory motion, cardiac motion, etc. For instance, thefirst involuntary motion phase and the second involuntary motion phasemay correspond to an end-inspiration and an end-expiration of arespiratory motion the subject, respectively. As another example, thefirst involuntary motion phase and the second involuntary motion phasemay correspond to an end-diastolic phase and an end-systolic phase of acardiac motion of the subject, respectively.

A third aspect of the present disclosure relates to a method for imageprocessing. The method may be implemented on at least one machine eachof which has at least one processor and storage. The method may includeone or more of the following operations. Imaging data from a scanning ofa subject may be obtained. A first motion signal of a first motion typeand a second motion signal of a second motion type may be obtained. Theimaging data may be divided, based on the first motion signal, intogroups of the first gated imaging data. A group of the first gatedimaging data may correspond to a motion phase of the first motion typeof the subject. A first group and a second group of the first gatedimaging data may correspond to a first motion phase and a second motionphase of the first motion type of spatial points of the subject. A firstgated image corresponding to the first motion phase of the first motiontype may be reconstructed using the first group of first gated imagingdata. A second gated image corresponding to the second motion phase ofthe first motion type may be reconstructed using the second group offirst gated imaging data. The first gated image and the second gatedimage may be registered to determine a motion vector field of the firstmotion type. The motion vector field of the first motion type mayinclude a plurality of motion vectors of the first motion type. A motionvector of the first motion type may indicate a motion of a spatial pointof the first motion type from the first motion phase to the secondmotion phase. For each spatial point, a first motion amplitude may bedetermined based on the corresponding motion vector field of the firstmotion type. The imaging data may be divided, based on the second motionsignal, into groups of the second gated imaging data. According tooperations similar to those with respect to the groups of the firstgated imaging data, for each spatial point, a second motion amplitudemay be determined based on the corresponding motion vector field of thesecond motion type. The imaging data may be gated according to dualgating based on the first motion signal and the second motion signal.The dual gating may be based on a locally adaptive gating approach. Foreach spatial point, a temporal spread function may be determined basedon the first motion amplitude and the second motion amplitude of thespatial point, a first resolution recovery of the first motion type, anda second resolution recovery of the second motion type. A dual gatedimage may be reconstructed from the locally adaptively gated imagingdata and the temporal spread functions.

A fourth aspect of the present disclosure relates to a system for imageprocessing. The system may include an acquisition module and aprocessing module. The acquisition module may obtain imaging data from ascanning of a subject. The acquisition module may obtain obtaining afirst motion signal of a first motion type and a second motion signal ofa second motion type. The processing module may include a gating controlunit and a reconstruction unit. The gating control unit may divide theimaging data, based on the first motion signal, into groups of the firstgated imaging data. A group of the first gated imaging data maycorrespond to a motion phase of the first motion type of the subject. Afirst group and a second group of the first gated imaging data maycorrespond to a first motion phase and a second motion phase of thefirst motion type of spatial points of the subject. The reconstructionunit may reconstruct a first gated image corresponding to the firstmotion phase of the first motion type using the first group of firstgated imaging data and a second gated image corresponding to the secondmotion phase of the first motion type using the second group of firstgated imaging data. The gating control unit may also register the firstgated image and the second gated image to determine a motion vectorfield of the first motion type. The motion vector field of the firstmotion type may include a plurality of motion vectors of the firstmotion type. A motion vector of the first motion type may indicate amotion of a spatial point of the first motion type from the first motionphase to the second motion phase. The gating control unit may further,for each spatial point, determine a first motion amplitude based on thecorresponding motion vector field of the first motion type, and dividethe imaging data based on the second motion signal. A group of thesecond gated imaging data may correspond to a motion phase of the secondmotion type of the subject. A first group and a second group of thesecond gated imaging data may correspond to a first motion phase and asecond motion phase of the second motion type of the spatial points ofthe subject. The gating control unit may determine, for each spatialpoint, a second motion amplitude based on the corresponding motionvector field of the second motion type according to the operationssimilar to those with respect to the groups of the first gated imagingdata. The gating control unit may gate, according to dual gating basedon the first motion signal and the second motion signal, the imagingdata. The dual gating may be based on a locally adaptive gatingapproach. For each spatial point, the gating control unit may assess atemporal spread function based on the first motion amplitude and thesecond motion amplitude of the spatial point. The reconstruction unitmay further reconstructing a dual gated image from the locallyadaptively gated imaging data and the temporal spread functions.

In some embodiments, the first motion type corresponds to a voluntarymotion, and the second motion type corresponds to an involuntary motion.In some embodiments, the involuntary motion may be a respiratory motion,a cardiac motion, etc.

In some embodiments, the registration of the first gated image and thesecond gated image to determine a motion vector field of the firstmotion type may include performing 2D image registration of the firstgated image and the second gated image; determining, for each spatialpoint, a 2D motion vector in a coronal plane and a 2D motion vector in asagittal plane based on the registration; and determining the motionvector field based on the 2D motion vectors in a coronal plane and a 2Dmotion vectors in a sagittal plane. The motion vector field may bethree-dimensional.

In some embodiments, the registration of the first image frame and thesecond image frame may include performing 3D image registration. Themotion vector field may be determined based on the 3D imageregistration. The motion vector field may be three-dimensional.

In some embodiments, the reconstruction of a dual gated image from thelocally adaptively gated imaging data and the temporal spread functionsmay include determining an intra-frame motion amplitude based on asystem intrinsic resolution; and determining a gate number correspondingto the second motion signal according to the locally adaptive gatingapproach based on the intra-frame motion amplitude.

In some embodiments, the gating control unit may further determine agating area corresponding to the first motion signal or the secondmotion signal; determining a plurality of gate numbers for a pluralityof voxels corresponding to spatial points included in the gating area;and determining the corresponding temporal spread functions for theplurality of voxels based on the plurality of gate numbers and themotion amplitudes of the spatial points corresponding to the firstmotion signal or the second motion signal. The gating area may bedetermined by one or more of the following operations. The second motionamplitudes of the second motion type of the spatial points may becompared with a first threshold that relates to a system intrinsicresolution. The gating area may be determined based on the comparison.In some embodiments, the gating area may be determined based on a userinput.

A further aspect of the present disclosure relates to systems forperforming the methods disclosed herein. A system may include at leastone processor and storage for storing instructions. The instructions,when executed by the at least one processor, may cause the system toperform a method disclosed herein.

A still further aspect of the present disclosure relates tonon-transitory computer readable media including executable instructionsfor implementing the methods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1-A is a schematic diagram illustrating an exemplary ECT systemaccording to some embodiments of the present disclosure;

FIG. 1-B is a block diagram illustrating an exemplary image processingsystem according to some embodiments of the present disclosure;

FIG. 2 is a block diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a block diagram illustrating an exemplary processing moduleaccording to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process forreconstructing an ECT image according to some embodiments of the presentdisclosure;

FIG. 5-A is a flowchart illustrating an exemplary process fordetermining gate numbers for reconstructing an ECT image according tosome embodiments of the present disclosure;

FIG. 5-B1 through FIG. 5-B3 are diagrams illustrating exemplary gatingapproaches based on motion amplitudes according to some embodiments ofthe present disclosure;

FIG. 6 is a schematic diagram illustrating an exemplary temporal spreadfunction according to some embodiments of the present disclosure;

FIG. 7-A through FIG. 7-C illustrate exemplary ECT images generated bydifferent image reconstruction methods according to some embodiments ofthe present disclosure;

FIG. 8-A through FIG. 8-C illustrate exemplary ECT images generated bydifferent image reconstruction methods according to some embodiments ofthe present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for determiningmotion amplitudes of spatial points of a subject according to someembodiments of the present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for selectingthe imaging data according to some embodiments of the presentdisclosure;

FIG. 11 illustrates an exemplary user interface for manually selectinggating area according to some embodiments of the present disclosure; and

FIG. 12 is a flowchart illustrating an exemplary process forreconstructing a dual gated image according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnosis or research purposes. In some embodiments,the imaging system may be an emission computed tomography (ECT) system,a magnetic resonance imaging (MRI) system, an ultrasonography system, amulti-modality system, or the like, or any combination thereof. The ECTsystem may include a positron emission tomography (PET) system, a singlephoton emission computed tomography (SPECT) system, etc. Exemplarymulti-modality system may include a computed tomography-positronemission tomography (CT-PET) system, a magnetic resonance-positronemission tomography (MR-PET) system, etc. In some embodiments, themulti-modality imaging system may include modules and/or components forperforming ECT imaging and/or related analysis.

For illustration purposes, the disclosure describes systems and methodsfor ECT image reconstruction. It is understood that this is forillustration purposes and not intended to limit the scope of the presentdisclosure.

The imaging system may reconstruct an ECT image based on a gatingapproach. As used herein, a gating approach may refer to that ECT datamay be divided into a plurality of sections and one of the sections maybe selected to be processed to generate an ECT image. For example, theimaging system may sort the ECT data acquired from a subject into aplurality of bins based on one or more gate numbers and reconstruct anECT image based on at least one of the plurality of bins. As anotherexample, the imaging system may reconstruct an ECT image by applyingdifferent gate numbers to the ECT data corresponding to differentspatial points of a subject. In the present disclosure, “gating number,”“gate number,” and “a number of gates” are used interchangeably.

The following description is provided to help better understanding ECTimage reconstruction methods or systems. The term “image” used in thisdisclosure may refer to a 2D image, a 3D image, a 4D image, or anyrelated image data (e.g., the ECT data, projection data corresponding tothe ECT data). Image data may also be referred to as imaging data. Theimage data may correspond to a distribution of an ECT tracer moleculeswithin the subject. As used herein, the ECT tracer may refer to asubstance that may undergo certain changes under the influence of anactivity and/or functionality within the subject, whose activity and/orfunctionality may be visualized and/or studied. This is not intended tolimit the scope the present disclosure. For persons having ordinaryskills in the art, a certain amount of variations, changes, and/ormodifications may be deducted under guidance of the present disclosure.Those variations, changes, and/or modifications do not depart from thescope of the present disclosure.

FIG. 1-A is a schematic diagram illustrating an exemplary ECT systemaccording to some embodiments of the present disclosure. The ECT systemmay include an ECT scanner 110 and a host computer 120. ECT scanner 110may include a gantry 111, one or more detectors 112, a detecting region113, and a subject table 114.

Detector 112 may detect radiation events (e.g., gamma photons) emittedfrom detecting region 113. In some embodiments, detector 112 may includea plurality of detector units which may forms a field of view of atotal-body PET scanner. The detector units may be implemented in anysuitable manner, for example, a ring, a rectangle, or an array. In someembodiments, the detector unit may include one or more crystal elementsand/or one or more photomultiplier tubes (PMT) (not shown). In someembodiments, a PMT as employed in the present disclosure may be asingle-channel PMT or a multi-channel PMT. Subject table 114 mayposition a subject in detecting region 113.

In some embodiments, the detected radiation events may be stored orarchived in a storage (e.g., a storage device in host computer 120),displayed on a display (e.g., a screen on host computer 120), ortransferred to any relating device (e.g., an external database). In someembodiments, a user may control ECT scanner 110 via host computer 120.

Further, while not shown, the ECT system may be connected to a network(e.g., a telecommunications network, a local area network (LAN), awireless network, a wide area network (WAN) such as the Internet, apeer-to-peer network, a cable network, etc.) for communication purposes.

It should be noted that the above description of the ECT system ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the ECT system may be varied or changed according tospecific implementation scenarios. Merely by way of example, some othercomponents may be added into the ECT system, such as a patientpositioning module, a gradient amplifier module, and other devices ormodules.

FIG. 1-B is a block diagram illustrating an exemplary image processingsystem 100 according to some embodiments of the present disclosure.Image processing system 100 may be implemented via host computer 120. Asillustrated in FIG. 1-B, image processing system 100 may include anacquisition module 131, a control module 132, a storage module 133, aprocessing module 134, and a display 135.

Acquisition module 131 may acquire or receive ECT data. The ECT data mayinclude SPECT data, PCT data, or CT data. The ECT data may be a dataset. In some embodiments, the ECT data may be list-mode data or sinogramdata. Merely by way of example with reference to a PET system,acquisition module 131 may acquire or receive PET data. In someembodiments, during a PET scan or analysis, PET tracer (also referred toas “PET tracer molecules”) are first introduced into the subject beforean imaging process begins. During the PET scan, the PET tracer moleculesmay emit positrons, namely the antiparticles of electrons. A positronhas the same mass and the opposite electrical charge as an electron, andit undergoes an annihilation (also referred to as an “annihilationevent” or a “coincidence event”) with an electron (that may naturallyexist in abundance within the subject) as the two particles collide. Anelectron-positron annihilation may result in two 511 keV gamma photons,which, upon their own generation, begin to travel in opposite directionswith respect to one another. The line connecting the two gamma photonsmay be referred to as a “line of response (LOR).” Acquisition module 131may obtain the trajectory and/or information of the gamma photons (alsoreferred to as the “PET data”). For example, the PET data may include alist of annihilation events, transverse and longitudinal positions ofthe LORs, or the like, or a combination thereof. In some embodiments,the PET data may be used to determine the locations and/or theconcentration distribution of the PET tracer molecules within thesubject.

In some embodiments, the PET tracer may include carbon (11C), nitrogen(13N), oxygen (15O), fluorine (18F), or the like, or a combinationthereof. In some embodiments, for a SPECT system, a SPECT tracer may beintroduced into the subject. The SPECT tracer may includetechnetium-99m, iodine-123, indium-111, iodine-131, or the like, or acombination thereof. Accordingly, in some embodiments, the PET tracer orSPECT tracer of the present disclosure may be organic compoundscontaining one or more of such isotopes. These tracers are eithersimilar to naturally occurring substances or otherwise capable ofinteracting with the functionality or activity of interest within thesubject. Hence, distributional information of the tracer may be reliablyused as an indicator of the subject functionality. In some embodiments,the PET tracer and the SPECT tracer may be collectively referred to as“ECT tracer.”

Control module 132 may generate a control parameter for acquisitionmodule 131, storage module 133, processing module 134, and display 135.For example, control module 132 may control acquisition module 131 as towhether to acquire a signal, or the time when a signal acquisition mayoccur. As another example, control module 132 may control processingmodule 134 to select different algorithms to process the ECT dataacquired by acquisition module 131. In some embodiments, control module132 may receive a real-time or a predetermined command provided by auser (e.g., a doctor) and adjust acquisition module 131, and/orprocessing module 134 to take images of a subject according to thereceived command. In some embodiments, control module 132 maycommunicate with the other modules in image processing system 100 forexchanging information or data.

Storage module 133 may store the acquired ECT data, the controlparameters, the processed ECT data, or the like, or a combinationthereof. In some embodiments, storage 133 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. For example, the massstorage may include a magnetic disk, an optical disk, a solid-statedrives, etc. The removable storage may include a flash drive, a floppydisk, an optical disk, a memory card, a zip disk, a magnetic tape, etc.The volatile read-and-write memory may include a random access memory(RAM). The RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. The ROM may includea mask ROM (MROM), a programmable ROM (PROM), an erasable programmableROM (PEROM), an electrically erasable programmable ROM (EEPROM), acompact disk ROM (CD-ROM), and a digital versatile disk ROM, etc. Insome embodiments, storage 133 may store one or more programs and/orinstructions that may be executed by the processor(s) of imageprocessing system 100 to perform exemplary methods described in thisdisclosure. For example, storage 133 may store program(s) and/orinstruction(s) executed by the processor(s) of image processing system100 to acquire ECT data, reconstruct an image based on the ECT data, ordisplay any intermediate result or a resultant image.

Processing module 134 may process different kinds of informationreceived from different modules in image processing system 100. In someembodiments, processing module 134 may process the ECT data acquired byacquisition module 131, or retrieved from storage module 133. In someembodiments, processing module 134 may reconstruct ECT images based onthe ECT data, generate reports including one or more ECT images and/orother related information, or the like. For example, processing module134 may process the ECT data based on a gating approach and reconstructan ECT image based on the gated ECT data. As another example, processingmodule 134 may determine a plurality of gate numbers for the ECT datacorresponding to a plurality of spatial points of the subject (e.g.,chest, back, or the like).

Display 135 may display any information relating to image processingsystem 100. The information may include programs, software, algorithms,data, text, number, images, voice, or the like, or any combinationthereof. In some embodiments, display 135 may include a liquid crystaldisplay (LCD), a light emitting diode (LED) based display, a flat paneldisplay, a cathode ray tube (CRT), a touch screen, or the like, or acombination thereof. The touch screen may include, for example, aresistance touch screen, a capacity touch screen, a plasma touch screen,a vector pressure sensing touch screen, an infrared touch screen, or thelike, or a combination thereof. In some embodiments, display 135 mayhave a window for selecting a gating area of a subject.

In some embodiments, one or more modules illustrated in FIG. 1-B may beimplemented in at least part of the exemplary ECT system illustrated inFIG. 1-A. For example, acquisition module 131, control module 132,storage module 133, processing module 134, and/or display or displaydevice 135 may be integrated into a console (not shown). Via theconsole, a user may set parameters for scanning, control the imagingprocedure, control a parameter of the reconstruction of an image, viewthe reconstructed images, etc. In some embodiments, the console may beimplemented via host computer 120.

FIG. 2 is a block diagram illustrating exemplary hardware and softwarecomponents of computing device 200 on which image processing system 100may be implemented according to some embodiments of the presentdisclosure. In some embodiments, computing device 200 may include aprocessor 202, a memory 204, and a communication port 206.

Processor 202 may execute computer instructions (program code) andperform functions of processing module 134 in accordance with techniquesdescribed herein. Computer instructions may include routines, programs,objects, components, data structures, procedures, modules, andfunctions, which perform particular functions described herein. Forexample, processor 202 may process the data or information received fromacquisition module 131, control module 132, storage module 133,processing module 134, or any other component of image processing system100. In some embodiments, processor 202 may include a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof. For example, processor 202 may include amicrocontroller to process the ECT data from ECT scanner 110 for imagereconstruction.

Memory 204 may store the data or information received from acquisitionmodule 131, control module 132, storage module 133, processing module134, or any other component of image processing system 100. In someembodiments, memory 204 may include a mass storage, a removable storage,a volatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drives, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,memory 204 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure. For example,memory 204 may store a program for processing module 134 forreconstructing an ECT image based on the ECT data.

Communication port 206 may transmit to and receive information or datafrom acquisition module 131, control module 132, storage module 133,processing module 134 via network. In some embodiments, communicationport 206 may include a wired port (e.g., a Universal Serial Bus (USB)port, a High Definition Multimedia Interface (HDMI) port, or the like)or a wireless port (a Bluetooth port, an infrared interface, a WiFiport, or the like).

FIG. 3 is a block diagram illustrating an exemplary processing module134 according to some embodiments of the present disclosure. Processingmodule 134 may include a pre-processing unit 302, a gating control unit304, a reconstruction unit 306, and a storage unit 308. In someembodiments, at least two of the units may be connected with each othervia a wired connection (e.g., a metal cable, an optical cable, a hybridcable, or the like, or any combination thereof) or a wireless connection(e.g., a Local Area Network (LAN), a Wide Area Network (WAN), aBluetooth, a ZigBee, a Near Field Communication (NFC), or the like, orany combination thereof).

Pre-processing unit 302 may process different kinds of informationreceived from acquisition module 131, control module 132, storage module133, and/or display 135. The information may include the ECT data, basicinformation regarding a subject, a control parameter (e.g., acquisitionfrequency, acquisition rate, or the like), a display parameter (e.g.,brightness, resolution, scale, or the like), or the like, or acombination thereof. Merely by way of example, pre-processing unit 302may process the ECT data, for example, to remove or reduce noises.

Gating control unit 304 may determine a gating parameter (e.g., a gatenumber) to gate the ECT data for image reconstruction. In someembodiments, the ECT data may be 4D data. As used herein, 4D data mayrefer to a data form containing time domain data and three dimensional(3D) spatial data. In some embodiments, the 4D data or a correspondingECT image reconstructed based on the 4D data may be expressed as λ(j,t), where j refers to a voxel (or an index) in the ECT image, the voxelcorresponds to a spatial point of the subject, and t refers to a timeaxis (or a time point on the time axis). As used herein, “gate” mayrefer to that the ECT data may be divided into a plurality of sectionsalong the time axis t and one of the sections may be selected to beprocessed to generate an ECT image. As used herein, “gate number” mayrefer to the number of the plurality of sections. In some embodiments,the coordinates of the time axis t may correspond to the gate number.For example, for a gate number n, the coordinates of the time axis t maybe {1, 2, 3, . . . , n}.

In some embodiments, during the acquisition of the ECT data, motions ofthe subject (e.g., respiratory motion or cardiac movements of the heart)may be unavoidable which may lead to motion blur in the ECT imagereconstructed based on the ECT data. In order to reduce the motion blur,gating control unit 304 may gate the ECT data according to a gate number(e.g., n) into a plurality of sections and select one of the sections toreconstruct an ECT image. In some embodiments, the gate number may bothinfluence the motion blur and the noise of the ECT image. For example,for a spatial point whose motion amplitude is A₀ (it is supposed thatA₀>ε, where ε is the intrinsic resolution of the imaging system), if thegate number is n, the motion blur of a voxel corresponding to thespatial point may be reduced to A₀/n, and the noise of the voxel may beincreased by √{square root over (n)}.

In some situations, for different spatial points of the subject, motioninformation (e.g., motion amplitude) may be different. For example, themotion amplitude by respiratory motion of a spatial point on the back ofa subject may be approximately zero, while the motion amplitude byrespiratory motion of a spatial point of the chest of the subject may berelatively high. Relative to an ECT image reconstructed based on anon-gating approach, motion blur or noise of an ECT image reconstructedbased on a gating approach may be modified. For example, for the ECTdata acquired from the chest of the subject, to reduce the possiblemotion blur of voxels corresponding to the chest in the ECT image, agate number of the gating approach may be determined based on the motionamplitude of the chest. In the ECT image, the motion blur of the voxelscorresponding to chest may be reduced, but the noise of the voxelscorresponding the chest may be reduced. In this situation, if a samegate number is selected for the ECT data acquired from the back of thesubject where the motion amplitude is approximately zero, the noise ofthe voxels corresponding to the back may be reduced.

In some embodiments, considering that the motion amplitudes of differentspatial points of a subject may be different, gating control unit 304may determine different gate numbers for different ECT data acquiredfrom different spatial points of the subject that correspond todifferent voxels in the ECT image, i.e. the number of gates of a regionare locally adaptive to local motion amplitude. As used herein, “locallyadaptive gating” indicates that imaging data corresponding to variousspatial points of a subject may be gated differently based on conditionsof the spatial points. In some embodiments, gate numbers determinedbased on the locally adaptive gating may integers. In some embodiments,gate numbers determined based on the locally adaptive gating maynon-integers (e.g., fractions, decimals, etc.). For instance, accordingto locally adaptive gating, the number of gates of the imaging datacorresponding to a region of the subject may be determined based on,e.g., the motion amplitudes of the spatial points within the region.Imaging data corresponding to two regions of the subject whose spatialpoints undergo motion of different motion amplitudes may be gateddifferently. The gating number determined based on locally adaptivegating may be referred to as an effective gating number. An effectivegating number may be an integer or a non-integer.

In some embodiments, gating control unit 304 may determine a motioncurve indicative of the motion amplitudes of different spatial points ofthe subject and determine different gate numbers based on the motioncurve. In some embodiments, while determining the plurality of gatenumbers, gating control unit 304 may take noise, motion blur, and userinput into consideration. In some embodiments, gating control unit 304may determine the gate numbers according to the motion amplitudes of thespatial points of the subject. In some embodiments, gating control unit304 may determine the gate numbers according to an intermediate imageagainst which value differences among voxels corresponding to thespatial points of the subject may be determined. Merely by way ofexample, a value of a voxel may refer to a grey level of the voxel. Insome embodiments, gating control unit 304 may determine a temporalspread function based on the gate numbers, and further reconstructionunit 306 may reconstruct an ECT image based on the temporal spreadfunction.

Reconstruction unit 306 may generate an ECT image relating to an object(e.g., a subject, or a portion thereof) based on the ECT data and thegate numbers. “Object” and “subject” may be used interchangeably in thepresent disclosure. For example, reconstruction unit 306 may gate theECT data based on the gate numbers and reconstruct the ECT image basedon the gated ECT data. In some embodiments, reconstruction unit 306 mayemploy different kinds of image reconstruction techniques for the imagereconstruction procedure. Exemplary image reconstruction techniques mayinclude Fourier slice theorem, filtered back projection algorithm,fan-beam reconstruction, iterative reconstruction, or the like, or acombination thereof. In some embodiments, reconstruction unit 306 mayinclude one or more sub-units (not shown). The sub-units may reconstructimages by employing different reconstruction techniques. In someembodiments, the reconstructed image may be stored in storage unit 308.

Storage unit 308 may store the ECT data processed by pre-processing unit302, the ECT image reconstructed by reconstruction unit 306, and thegating parameters determined by gating control unit 304. In someembodiments, the storage format may include text, picture, audio, video,code, or the like, or a combination thereof. In some embodiments, one ormore algorithms that may be used during the processing, thereconstruction, or the gating control process may be stored in storageunit 308. The algorithm may include a threshold segmentation algorithm,an iterative algorithm, an interpolation algorithm, a statisticalalgorithm, a smoothing filtering algorithm, or the like, or anycombination thereof.

It should be noted that the above description of processing module 134is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the assembly and/or function of processingmodule 134 may be varied or changed. In some embodiments, one or moreunits in processing module 134 may include an independent storage block(not shown) respectively and storage unit 308 may be optional. In someembodiments, any two or more units may be integrated into an independentunit used to implement more than one functions. As another example,pre-processing unit 302 may be optional.

FIG. 4 is a flowchart illustrating an exemplary process forreconstructing an ECT image according to some embodiments of the presentdisclosure. In 402, processing module 134 may determine projection datacorresponding to a plurality of voxels. In some embodiments, theprojection data may be obtained by a single-bed whole-body PET scanner.The single-bed whole-body PET scanner may scan the subject to obtainprojection data relating to a whole (total) body of a subject in a wholefield of view (FOV). The whole field of view may cover the whole body ofthe subject. In some embodiments, the whole field of view may include aplurality of local volumes of interest (VOIs). A local VOI may cover apart or region of the subject. The plurality of voxels may correspond toa plurality of spatial points of a subject. In some embodiments, theprojection data may be 4D data. As used herein, 4D data may refer to adata form containing time domain data and three dimensional (3D) spatialdata. In some embodiments, processing module 134 may determine theprojection data based on the ECT data acquired by acquisition module110.

In 404, gating control unit 304 may determine a plurality of gatenumbers for the plurality of voxels, where at least two of the pluralityof gate numbers may differ from each other. The plurality of gatenumbers may be associated with motion information of the plurality ofvoxels. In some embodiments, gating control unit 304 may determine thegate numbers according to the motion amplitudes of the spatial points ofthe subject corresponding to the plurality of voxels (e.g., a motioncurve). For example, the motion amplitudes of the spatial points may bedetermined or obtained from a data library or a population-baseddistribution approach. Under the data library or the population-baseddistribution approach, the respiratory motion and/or cardiac movement ofthe heart may be considered similar among human beings. The motionamplitudes of the spatial points of a subject may be determined based onstatistical data or clinical data. As another example, the motionamplitudes of the spatial points may be determined based on an imageregistration approach. Details about the image registration approach todetermine the motion amplitudes may be found elsewhere in the presentdisclosure (e.g., in the description of FIG. 9).

Further, under the data library or the population-based distributionapproach, the information of respiratory motion may be classifiedaccording to a criterion (e.g., age, gender, height, weight, or thelike, or a combination thereof). The cardiac movement information of theheart may be handled similarly. Under the data library or thepopulation-based distribution approach, the information of the cardiacmovement of the heart may be classified according to a criterion (e.g.,age, gender, height, weight, or the like). The motion amplitudes of thespatial points of a subject may be determined with an improved accuracy.

Merely by way of example, gating control unit 304 may determine theplurality of gate numbers by equation (1) below, that is, each of theplurality of gate numbers is a ratio of the motion amplitude of aspatial point corresponding to a voxel to the intrinsic resolution:

n(j)=A ₀(j)/ε,  (1)

where j refers to the voxel index, A₀(j) refers to the motion amplitudeof a spatial point corresponding to voxel j, ε refers to the intrinsicspatial resolution of the ECT system, and n(j) refers to the gate numberfor the voxel j.

In some embodiments, for different voxels, suitable gate numbers may bedifferent under different situations. For example, if the ECT image isused for a noise-sensitive application, a suitable gate number for thevoxel j may be less than A₀(j)/ε. If the image is used for aquantitative-sensitive application, a suitable gate number for voxel jmay be greater than A₀(j)/ε. Therefore, the number of gates may beselected based on factors including, for example, desired image quality.

In some embodiments, gating control unit 304 may determine the pluralityof gate numbers based on an intermediate image. In some embodiments, theECT data acquired from different spatial points corresponding todifferent voxels in the intermediate image may be gated according to auniform gate number and the intermediate image may be generated based onthe gated ECT data. As used herein, the intermediate image may be a 4Dimage. Based on the intermediate image, a difference between a voxel inthe intermediate image at a first time point and a corresponding voxelat a second time point may be determined. As used herein, correspondingvoxels may refer to voxels at different time points that correspond to asame spatial point of a subject in the intermediate image. In someembodiments, a difference image may be determined based on thedifference. Gating control unit 304 may determine a plurality of gatenumbers for the plurality of voxels based on the difference image. Forexample, the larger the value (e.g., a grey level) of a specific voxelin the difference image is, the greater the gate number may be for thecorresponding voxel.

In some embodiments, gating control unit 304 may further determine afeature relating to motion information of the plurality of voxels. Forexample, gating control unit 204 may determine a temporal spreadfunction based on the plurality of locally adaptive number of gates, andthe feature relating to the motion information may be a Full Width atHalf Maximum of the temporal spread function. In some embodiments, thetemporal spread function may refer to a blurring effect (e.g., aGaussian blur). In some embodiments, the temporal spread function mayrelate to spatial information and time information of the ECT data (orthe projection data) acquired from different spatial points of asubject.

In 406, reconstruction unit 306 may generate an ECT image based on theprojection data and the plurality of gate numbers. In some embodiments,the pre-processing unit 302 may select the projection data. For example,the subject may have a voluntary motion and an involuntary motion duringthe scanning. The voluntary motion of the subject may refer to a motionthat can be voluntarily controlled by the subject (e.g., a motion of thehead, a leg, a foot, etc.). The involuntary motion may refer to a motionthat cannot be voluntarily controlled by the subject (e.g., motions of alung, the heart, etc.). If an amplitude of the voluntary motion exceedsa voluntary motion threshold during a time period, the projection datacollected in the time period may be omitted in the image reconstruction.Details about the imaging data selection may be found elsewhere in thepresent disclosure (e.g., in the description of FIG. 10).

In some embodiments, the gating control unit 304 may determine a gatingarea for the subject in the whole (total) field of view. In someembodiments, the determination may be operated based on a scout image.The scout image may include an image reconstructed using one or moregroups of the gated imaging data. For example, the scout image may be afused image of the first gated image and the second gated image. In someembodiments, the determination may be based on the determined motionamplitudes of spatial points of the subject automatically or implementedin response to one or more gating instructions by the user. In someembodiments, the gating control unit 304 may determine different gatenumbers for different parts of the subject. Each part of the subject mayhave a local gating number. For example, for each local VOI of thesubject, the gating control unit 304 may determine a corresponding localgating number. For a single-bed whole-body PET scanner, the local gatingnumbers of various parts or regions of the subject may be determinedbased on the motion amplitudes independently. That is, the determinationof the gating number of a part or region may be performed independentlyfrom the determination of the gating number of another part.

In some embodiments, the gating area may be determined automatically.For example, the processing module 134 may determine spatial pointshaving an involuntary motion. Then the processing module 134 maydetermine the gating area including spatial points having theinvoluntary motion or whose motion amplitudes exceed an involuntarymotion threshold.

In some embodiments, the gating area may be determined in response tothe one or more gating instructions provided by the user. For example,the user may determine the gating area based on the motion amplitudesdetermined before, and give the gating instruction to the gating controlunit 304 via a user interface (e.g., in the description of FIG. 11).

In some embodiments, gating control unit 304 or reconstruction unit 306may gate the projection data based on the plurality of gate numbers. Forexample, for a specific voxel, gating control unit 304 or reconstructionunit 306 may gate the projection data acquired from a spatial pointcorresponding to the specific voxel along the time axis according to acorresponding gate number of the plurality of gate numbers; gatedprojection data may include the projection data for voxels and theirrespective gate numbers; reconstruction unit 306 may reconstruct an ECTimage based on the gated projection data.

In some embodiments, the plurality of gating numbers may be assigned tothe image data based on the motion amplitudes of spatial pointscorresponding to the image data. For instance, only image datacorresponding to spatial points with a gating area are to be gated; aplurality of gating numbers are assigned to the image data within thegating area based on the motion amplitudes of the corresponding spatialpoints within the gating area.

In some embodiments, reconstruction unit 306 may reconstruct one or moregated ECT images based on the temporal spread function. In someembodiments, reconstruction unit 306 may generate the gated ECT imagesbased on an image reconstruction algorithm. The image reconstructionalgorithm may include Maximum Likelihood Expectation Maximization (MLEM)algorithm, Ordered Subset Expectation Maximization (OSEM) algorithm,Maximum Likelihood reconstruction of Attenuation and Activity (MLAA)algorithm, or the like, or a combination thereof.

Merely by way of example with reference to the reconstruction of gatedPET images, the distribution of the projection data of the voxels may beapproximated by a Poisson distribution, and a likelihood function of thedistribution of the projection data may be described by equation (2):

(x,p)=Π_(i)({circumflex over (p)} _(i))^(p) ^(i) (p_(i)!)⁻¹exp(−{circumflex over (p)} _(i)),  (2)

where

(x, p) refers to the likelihood function of the distribution of theprojection data, x refers to the distribution of the projection data p,i refers to the i^(th) LOR of the projection data, and {circumflex over(p)}_(i) refers to an estimation of the projection data of the i^(th)LOR.

In some embodiments, the projection data may be estimated based on aprojection matrix of the imaging system, an estimation of thedistribution of the PET tracer within the subject, scattering events, orrandom events. As used herein, the projection matrix may be determinedbased on default settings of the imaging system, or provided by a user.The scattering events and random events may be determined based onstatistical data or empirical data. For example, the estimation of theprojection data may be determined by equation (3):

{circumflex over (p)}=HF+S+R,  (3)

where {circumflex over (p)} refers to the estimation of the projectiondata, H refers to the projection matrix of the ECT system, F refers to avector of an estimated PET image corresponding to an estimation of thedistribution of the PET tracer within the subject (generally in an ECTprocess, F refers to an estimated ECT image), S refers to scatteringevents, and R refers to the random events.

In some embodiments, the estimated ECT images may be determined based ona first substituted ECT image by a first interpolation function. As usedherein, the first substituted ECT image may refer to an ECT imagecorresponding to the distribution of the ECT tracer within the subject.For different voxels (or different spatial points of the subject) in thefirst substituted ECT image, the coordinates of the time axis may bedifferent; that is, for different voxels, the lengths of the time axismay be different. For example, for voxel j, the coordinates of the timeaxis may be {1, 2, 3, . . . , n(j)}, and the length of the time axis maybe n(j), where n(j) is an integer.

For example, the estimated ECT image may be determined based on thefirst substituted ECT image by equation (4):

F(j,g)=Σ_(t=1) ^(n(j)) u _(j)(g,t)λ(j,t),  (4)

where j refers to the voxel index, g and t refer to temporal indexes(also referred to as the “coordinates of the time axis”), F(j, g) refersto the estimated ECT image, λ(j, t) refers to the first substituted ECTimage, and u_(j)(g, t) refers to the first interpolation function.

In some embodiments, in the estimated ECT images determined based on thefirst substituted ECT image, for different voxels (or different spatialpoints of the subject), the coordinates of the time axis is uniform;that is, for different voxels, the lengths of the time axis is uniform(i.e., G, the maximum one of the plurality of gate numbers (alsoreferred to as a “threshold relating to the plurality of gate numbers)).For example, for voxel j, the length of the time axis is G, and the gatenumber may be n(j), where n(j) may be an integer or not. Under actualoperation, an integer is suitable for the value of n(j), while undertheoretical case, a non-integer is suitable for the value of n(j).

In some embodiments, the first interpolation function may include alinear interpolation function, a cubic interpolation function, a splineinterpolation function, or the like, or a combination thereof. Forexample, the first interpolation function may be expressed as equation(5):

$\begin{matrix}{{u_{j}\left( {g,t} \right)} = \left\{ \begin{matrix}{0,{t \neq \left\lfloor \frac{g*{n(j)}}{G} \right\rfloor},} & {t \neq {\left\lfloor \frac{g*{n(j)}}{G} \right\rfloor + 1}} \\{\left\{ \frac{g*{n(j)}}{G} \right\},} & {{t = {\left\lfloor \frac{g*{n(j)}}{G} \right\rfloor + 1}},} \\{{1 - \left\{ \frac{g*{n(j)}}{G} \right\}},} & {t = \left\lfloor \frac{g*{n(j)}}{G} \right\rfloor}\end{matrix} \right.} & (5)\end{matrix}$

where j refers to the voxel index, g and t refer to temporal indexes,n(j) refers to the gate number determined for the j^(th) voxel (alsoreferred to as the “length of the time axis”), G refers to the maximumgate number of the plurality of gate numbers, symbol └x┘ refers to afunction for determining a maximum integer which is less than or equalto x, and symbol {x} refers to a function for determining a fractionalpart of x, that is, {x}=x−└x┘.

In some embodiments, the first substituted ECT image may be determinedbased on a second substituted ECT image by a second interpolationfunction. For different voxels in the second substituted ECT image, thecoordinates of the time axis may be uniform; that is, for differentvoxels, the lengths of the time axis may be uniform (G, the maximum oneof the plurality of gate numbers). For voxel j, the length of the timeaxis is G, and the gate number may be n(j), where n(j) is an integer ornot. For example, the first substituted ECT image may be determined byequation (6):

λ(j,t)=Σ_(τ=1) ^(G)(v _(j)(t,τ)f(j,τ)),  (6)

where j refers to the voxel index, τ and t refer to temporal indexes, Grefers to the uniform length of the time axis (i.e., the maximum one ofthe plurality of gate numbers), λ(j, t) refers to the first substitutedECT image, v_(j)(t, τ) refers to the second interpolation function, andf(j, τ) refers to the second substituted ECT image.

In some embodiments, considering that the first substituted ECT imagemay be determined by the second substituted ECT image, there may be arelationship between the estimated ECT image and the second substitutedECT image. For example, the estimated ECT image and the secondsubstituted ECT image may be linked by a temporal spread function asexpressed in equation (7):

F(j,g)=Σ_(τ=1) ^(G) w _(j)(g,τ)f(j,τ),  (7)

where j refers to the voxel index, g and τ refer to temporal indexes, Grefers to the uniform length of the time axis (i.e., the maximum gatenumber of the plurality of gate numbers), F(j, g) refers to theestimated ECT image, f(j, τ) refers to the second substituted ECT image,and w_(j)(g, τ) refers to the temporal spread function.

In some embodiments, the temporal spread function may be determined bythe first interpolation function. For example, the estimated ECT imagemay be determined by equation (8):

F(j,g)=Σ_(t=1) ^(n(j)) u _(j)(g,t)λ(j,t)=Σ_(t=1) ^(n(j)) u_(j)(g,t)Σ_(τ=1) ^(G)(v _(j)(t,τ)f(j,τ))=τ_(τ=1) ^(G)(g,τ)f(j,τ),  (8)

where j refers to the voxel index, g, τ, and t refer to temporalindexes, G refers to the uniform length of the time axis (i.e., themaximum gate number of the plurality of gate numbers), F(j, g) refers tothe estimated ECT image, λ(j, t) refers to the first substituted ECTimage, u_(j)(g, t) and v_(j)(t, τ) refer to the first interpolationfunction and the second interpolation function, respectively, f(j, τ)refers to the second substituted ECT image, and w_(j)(g, τ) refers tothe temporal spread function.

Therefore, the temporal spread function may be determined by equation(9):

w _(j)(g,τ)=Σ_(t=1) ^(n(j)) u _(j)(g,t)v _(j)(t,τ),  (9)

where j refers to the voxel index, g and t refer to temporal indexes,u_(j)(g, t) and v_(j)(t, τ) refer to the first interpolating functionand the second interpolating function, respectively, n(j) refers to thegate number determined for the j^(th) voxel, and w_(j)(g, τ) refers tothe temporal spread function. For voxel j, the gate number n(j) may notbe an integer. For instance, the gate number n(j) may be a fraction or adecimal.

In some embodiments, the temporal spread function may be determinedbased on the plurality of gate numbers. In some embodiments, thetemporal spread function may be determined by a blurring function (e.g.,a Gaussian blurring function). In some embodiments, the Full Width atHalf Maximum (FWHM) of the blurring function may equal to G/n(j). Forexample, the time spread function may be determined by equation (10):

$\begin{matrix}{{{w_{j}\left( {g,\tau} \right)} = {\frac{1}{C_{g}}{\exp \left( {{- {\ln (2)}}\; \frac{4\left( {\tau - g} \right)^{2}}{\left( {G/{n(j)}} \right)^{2}}} \right)}}},} & (10)\end{matrix}$

where j refers to the voxel index, g and τ refer to temporal indexes, Grefers to the uniform length of the time axis (i.e., the maximum gatenumber of the plurality of gate numbers), n(j) refers to the gate numberfor the j^(th) voxel, and C_(g) refers to a constant value. C_(g) may bedetermined by Σ_(τ)w_(j)(g, τ)=1. As another example, the temporalspread function may be determined by equation (11):

$\begin{matrix}{{w_{j}\left( {g,\tau} \right)} = \left\{ {\begin{matrix}{1,} & {\tau = g} \\{0,} & {Otherwise}\end{matrix},} \right.} & (11)\end{matrix}$

where j refers to the voxel index, and g and τ refer to temporalindexes.

In some embodiments, the first substituted ECT image may be determinedby combining equation (3) and equation (4) with the MLEM algorithm. Aniterative function for the first substituted ECT image may be determinedby equation (12):

$\begin{matrix}{{\lambda_{j,t}^{m + 1} = {\frac{\lambda_{j,t}^{m}}{\sum_{g}{u_{j,g,t}{\sum_{i}H_{i,j}}}}{\sum_{g}{u_{j,g,t}{\sum_{i}\frac{H_{i,j}P_{i,g}}{{\sum_{k}{H_{i,k}{\sum_{\tau}{u_{k,g,t}\lambda_{k,t}^{m}}}}} + S_{g} + R_{g}}}}}}},} & (12)\end{matrix}$

where j and k refer to voxel indexes, g, τ, and t refer to temporalindexes, m refers to the iterative index, u refers to the firstinterpolating function, H refers to the projection matrix of the ECTsystem, S refers to the scattering events, R refers to the randomevents, P refers to the projection data, and refers to the firstsubstituted ECT image.

In some embodiments, the second substituted ECT image may be determinedby combining equation (4) and equation (7) with the MLEM algorithm. Aniterative function for the second substituted ECT image may bedetermined by equation (13):

$\begin{matrix}{{f_{j,\tau}^{m + 1} = {\frac{f_{j,\tau}^{m}}{\sum_{g}{u_{j,g,\tau}{\sum_{i}H_{i,j}}}}{\sum_{g}{w_{j,g,\tau}{\sum_{i}\frac{H_{i,j}P_{i,g}}{{\sum_{k}{H_{i,k}{\sum_{\tau}{w_{k,g,\tau}f_{k,\tau}^{m}}}}} + S_{g} + R_{g}}}}}}},} & (13)\end{matrix}$

where j and k refer to voxel indexes, g, τ, and t refer to temporalindexes, m refers to the iterative index, w refers to the temporalspread function, H refers to the projection matrix of the ECT system, Srefers to the scattering events, R refers to the random events, P refersto the projection data, and f refers to the second substituted ECTimage.

In some embodiments, the iterative function may begin with a uniformdistribution estimation. To identify a difference between the estimatedprojection data and the actually measured projection data, they may becompared during the iteration process. During the iterative process, theestimated projection data may be updated and a new iteration may beperformed. The difference between the estimated projection data and theactually measured projection data may be reduced during the iterativeprocess. In some embodiments, the iterative process may proceed untilthe difference between the estimated projection data and the actuallymeasured projection data is less than a threshold value. In someembodiments, the iterative process may proceed until the differencebetween the estimated projection data and the actually measuredprojection data stables—the change of the differences between a certainnumber (e.g., 2, 3, 4) of consecutive iterations falls within athreshold value. In some embodiments, the iterative process may proceeduntil the number of iterations that have been performed exceeds athreshold value. The threshold value may be determined based on defaultsettings of imaging system, or provided by a user.

In some embodiments, the estimated ECT image may be determined based onthe first substituted ECT image or the second substituted ECT image. Insome embodiments, image processing system 100 may generate an ECT imagebased on the estimated ECT image, the first substituted ECT image,and/or the second substitute ECT image.

FIG. 5-A is a flowchart illustrating an exemplary process fordetermining a plurality of gate numbers for a plurality of voxelsaccording to some embodiments of the present disclosure. In 502, gatingcontrol unit 304 may generate an intermediate image based on theprojection data corresponding to a plurality of voxels. For example,gating control unit 304 may gate the projection data according to auniform gate number for the plurality of voxels.

In 504, gating control unit 304 may determine a difference between avoxel at a first time point and a corresponding voxel at a second timepoint based on the intermediate image. As used herein, correspondingvoxels may refer to voxels at different time points that correspond to asame spatial point of a subject in one or more intermediate imagescorresponding to the one or more time points. For example, thedifference may be determined by x(j, g)−x(j, t), where j refers to thevoxel index, g and t refer to temporal indexes, x(j, t) refers to thevalue (e.g., a grey level) of the j^(th) voxel at time point t, and x(j,g) refers to the value (e.g., a grey level) of the j^(th) voxel at timepoint g.

In 506, gating control unit 304 may determine a difference image basedon the difference determined in 504. For example, the difference imagemay be determined by equation (14):

D(j,t)=√{square root over (Σ_(g=1) ^(G)(x(j,g)−x(j,t))²)}/Σ_(g=1,g≠t)^(G) x(j,g),  (14)

where j refers to the voxel index, g and t refer to temporal indexes, Grefers to the uniform length of the time axis (i.e., the maximum one ofthe plurality of gate numbers), x(j, t) refers to the value of thej^(th) voxel at time point t, x(j, g) refers to the value of the j^(th)voxel at time point g, and D(j, t) refers to the difference image.

In 508, gating control unit 304 may determine a plurality of gatenumbers for the plurality of voxels based on the difference image. Forexample, for the j^(th) voxel, the larger the value of the voxel at timepoint tin the difference image is, the greater the gate number may befor the j^(th) voxel. For example, the gate number for the j^(th) voxelmay be determined by equation (15):

$\begin{matrix}{{{n(j)} = {G*\frac{\max\limits_{t}\left( {D\left( {j,t} \right)} \right)}{\max\limits_{t,j}\left( {D\left( {j,t} \right)} \right)}}},} & (15)\end{matrix}$

where n(j) refers to the gate number for the j^(th) voxel, G refers tothe uniform length of the time axis (i.e., the maximum gate number ofthe plurality of gate numbers), and D(j, t) refers to the value of thej^(th) voxel at time point tin the difference image.

After the plurality of gate numbers are determined, gating control unit304 may further determine the temporal spread function according toequation (10).

In some embodiments, the temporal spread function illustrated in FIG. 6may be determined based on the difference image. For example, the largerthe value of the j^(th) voxel at time point tin the difference image is,the lower the value of the FWHM of the temporal spread function may be.

FIG. 5-B1 through FIG. 5-B3 are diagrams illustrating exemplary gatingapproaches based on motion amplitudes according to some embodiments ofthe present disclosure. In the three diagrams, the horizontal axisrepresent spatial coordinates. The spatial points of the subjectcorresponding to the voxels in reconstructed images may distribute alongthe spatial coordinates. The solid-line vertical axis representseffective gate numbers. The dashed-line vertical axis represents motionamplitudes. A solid line represents a distribution of the effective gatenumbers at various spatial points. A dashed line represents adistribution of the motion amplitudes corresponding to various spatialpoints. The distribution of motion amplitudes may be obtained using, forexample, the methods disclosed in the present disclosure.

FIG. 5-B1 illustrates a conventional gating approach. For the spatialpoints, the corresponding effective gate numbers may be determined as aconstant value despite various motion amplitudes of the spatial points.

FIG. 5-B2 illustrates a locally adaptive gating approach with an integergating number. The effective gate numbers may be determined based on themotion amplitudes. As shown in the figure, a higher motion amplitude maycorrespond to a larger effective gate number. Various motion amplitudeswithin a range may correspond to a same effective gate number. Aneffective gate number is an integer. Details about the locally adaptivegating may be found elsewhere in the present disclosure (e.g., in thedescription of FIG. 4).

FIG. 5-B3 illustrates a locally adaptive gating approach with anon-integer gating number determined based on temporal spread functionsof the spatial points. A gating number so determined may be anon-integer. Compared to the solid step-curve illustrated in FIG. 5-B2,the solid continuous curve illustrated in FIG. 5-B3 follows the curve ofthe motion amplitudes more accurately.

FIG. 6 is a schematic diagram illustrating an exemplary temporal spreadfunction according to some embodiments of the present disclosure. Asillustrate in FIG. 6, the curve may donate an exemplary temporal spreadfunction for the j^(th) voxel varying with time t. The FWHM of the curveequals to G/n(j). The greater the value of the FWHM is, the smoother thecurve of the temporal function may be. In some embodiments, the value ofthe FWHM may be determined based on the motion amplitude of the spatialpoint of the subject corresponding to a voxel. For example, the value ofthe FHWM may be determined based on the difference image described withreference to FIG. 5-A. In some embodiments, the FWHM may be determinedby G/n(j), where G refers to the uniform length of the time axis (i.e.,the maximum gate number of the plurality of gate numbers), and n(j)refers to the gate number for the j^(th) voxel.

FIG. 9 is a flowchart illustrating an exemplary process 900 fordetermining motion amplitudes of spatial points of a subject accordingto some embodiments of the present disclosure. In some embodiments, theprocess 900 may be applied in connection with the ECT imagingtechniques.

In 902, the processing module 134 may obtain imaging data from ascanning of a subject. In some embodiments, the scanning may include anECT scanning. The imaging data may include the projection data describedelsewhere in the present disclosure. See, for example, relevantdescription with reference to FIG. 4. In some embodiments, theprojection data may include PET projection data, sonogram data, listmode data, etc. Merely by way of example, the imaging data may be 4Ddata and stored in a list mode. For example, the imaging data may bearranged based on the time axis.

In 904, the gating control unit 304 may gate (or referred to as divide)the imaging data into a plurality of groups (or referred to as frames)of gated imaging data. The gating numbers may be any positive number. Insome embodiments, the gating numbers may be determined empirically for asubject. Each of the groups of gated imaging data may be used toreconstruct an image. In some embodiments, the processing module 134 maynumber the groups. Different groups may correspond to different timeperiods of a motion (e.g., a voluntary motion, an involuntary motion, orboth).

For example, different groups may correspond to different time periodsof a respiratory motion; group 1 may correspond to an end period ofinspiration motion (also referred as end inspiration phase); group N maycorrespond to an end period of expiration motion (also referred asend-expiration phase); a group between group 1 and group N, e.g., group2, . . . , group (N−1), may correspond to a period between the endperiod of inspiration motion and the end period of expiration motion.Similarly, in a cardiac motion, a group may correspond to anend-diastolic phase, and a different group may correspond to anend-systolic phase. As another example, different groups may correspondto different time periods of a voluntary motion; group 1 may correspondto a starting period of a head motion of the subject; group N maycorrespond to an end period of the head motion; a group between group 1and group N, e.g., group 2, . . . , group (N−1), may correspond to aperiod between the starting period and the end period of the headmotion. In some embodiments, different groups may correspond todifferent time periods of a voluntary motion and concurrently aninvoluntary motion.

In 906, the reconstruction unit 306 may reconstruct a first gated imageusing the first group of gated imaging data and a second gated imageusing the second group of gated imaging data. The first gated image andsecond gated image may also be referred to as intermediate images. Asused herein, an intermediate image may refer to one reconstructed basedon gated raw imaging data. The first group of gated imaging data or thefirst gated image may correspond to the same spatial points in thescanning of the subject.

In some embodiments, the first group of gated imaging data and thesecond group of gated imaging data may correspond to characteristicperiods of the respiratory motion of a subject. For example, the firstgroup of gated imaging data and the second group of gated imaging datamay correspond to the end period of an inspiration motion and the endperiod of an expiration motion of the respiratory motion, respectively.The motion amplitude of a spatial point corresponding to the twocharacteristic periods may be maximum within a cycle of the respiratorymotion of the subject.

Merely by way of example, the imaging data may be the PET projectiondata. The reconstruction of the PET image may be performed based on anOS-EM algorithm. There may be attenuation of the projection data becauseof loss of detection of true coincidence events. When the photons passthrough the tissue to reach the detector 112 (e.g., a PET detector),part of the positive electrons may reach the detector 112, and the restof the photons may be scattered or absorbed by the tissue of thepatient. And the photons scattered or absorbed may cause the attenuationof the photon ray which in turn may contribute to the attenuationartifacts in the PET image. In the PET/CT system, x-rays from a CT scanmay be used to construct an attenuation map throughout the body, or aportion thereof. The attenuation map may be used to correct theattenuation in the PET data. Attenuation-mismatch artifacts may bepresent due to continuous respiration during both the PET and CT scans.Attenuation-mismatch artifacts may appear when the CT scan whose dataare used to construct the attenuation map corresponds to a differentmotion phase than the PET scan whose data are used to produce a PETimage based on the attenuation map. In some embodiments, the attenuationmap used in the reconstruction algorithm may be modified by filling aregion of the attenuation map that corresponds to a portion (e.g., thelung, the heart) of the subject having a relatively large motion withthe attenuation coefficient of a soft tissue to avoid or reduce theattenuation-mismatch artifacts.

In some embodiments, the reconstructed image may be 2D images in acoronal plane and a sagittal plane. In some embodiments, thereconstructed image may be 3D images. It is understood that 2D images inthe coronal plane and in the sagittal plane are mentioned forillustration purposes and not intended to limit the scope of the presentdisclosure. For a specific motion type, images in one or more planesdescribing the motion may be used. The 3D image may include a coronalview image, a sagittal view image, a transverse view image, or a view atan oblique angle. In some embodiments, the motion vectors in coronal andsagittal planes may be used to determine the motion amplitudes. In someembodiments, the motion vectors in the coronal and sagittal planes maybe determined based on 2D maximum intensity projection (MIP) images inthe coronal view and in the sagittal view generated from a 3Dreconstructed image.

In 908, the processing module 134 may register the first gated image andthe second gated image to determine the motion vector field. The firstgated image and second gated image may be 2D images or 3D images. Themotion vector field may be a 2D motion vector field or a 3D motionvector field. For instance, 2D images corresponding to two motion phasesof a motion type may be subject to 2D registration to obtain a 2D motionvector field in the coronal plane and a 2D motion vector field in thesagittal plane. The 2D motion vector field in the coronal plane and the2D motion vector field in the sagittal plane may be used to compose a 3Dmotion vector field including a plurality of 3D motion vectors. Asanother example, 3D images may be subject to 3D registration to providea 3D motion vector field. Various registration algorithms may be used.For instance, both rigid registration and non-rigid registration may beperformed.

The motion vector field may include the plurality of motion vectors. Theplurality of motion vectors may be 3D vectors. Each of the motionvectors may indicate a motion of a spatial point between differentmotion phases as represented in the first gated image and the secondgated image.

For instance, the registration algorithm may include a B-spline imageregistration algorithm. An exemplary cost function of the B-splineregistration is:

E(m)=D(I,T(J,m))+α∥Δm∥ ²,  (16)

where D(I, T(J, m)) refers to a difference between the images I and J, Iand J refers to the two images to be registered, m refers to the motionvector field, T(J, m) refers to image J transformed using the motionvector field m, α is a positive scalar for the smoothing term ∥Δm∥²; andΔ is the Laplace operator. The difference between two images may beassessed in terms of a parameter such as, for example, the grey valuesof pixels/voxels in images, or the intensity distribution patterns inthe images, etc. D(I, T(J, m)) may be in the form of the sum of squareddifferences (SSD), the sum of absolute difference (SAD), mutualinformation (MI), etc., with respect to the parameter. The processingmodule 134 may further determine the first motion vector and the secondmotion vector for each of the spatial points (e.g., the vector m_(c) andvector m_(s) in equation (18)) based on the motion vector field m. D(I,T(J, m)) may be determined by equation (17):

D(I,T(J,m))=Σ_(i)(I(x _(i))−J(x _(i) +m(x _(i))))²,  (17)

where I(x_(i)) refers to reference image, m(x_(i)) refers to motionvector field from J to I, J(x_(i)+m(x_(i)) refers to transformed image Jusing motion vector field m, x_(i) refers to coordinate of voxel i, andi refers to voxel index in image space.

In 910, for each spatial point, the processing module 134 may determinea motion amplitude based on the motion vector field. In someembodiments, the motion amplitude of a spatial point may be determinedbased on the first motion vector and the second motion vector. In someembodiments, the motion amplitudes may be determined by equation (18):

A ₀(j)=√{square root over (∥m _(c)(j)∥·∥m _(s)(j)∥)}A _(e)(j),  (18)

where j refers to the voxel index, A₀(j) refers to the motion amplitudeof a spatial point corresponding to voxel j, m_(c)(j) refers to the 2Dmotion vector in the coronal plane, m_(s)(j) refers to the 2D motionvector in the sagittal plane, and A_(e)(j) refers to an predeterminedmotion amplitude. In some embodiments, the predetermined motionamplitude A_(e)(j) may be determined by the user based on priorexperience. The value range of the A_(e)(j) is 0 to 1. Merely by way ofexample, based on empirical information, the amplitude for therespiratory motion of a human subject is: A_(e)(j) for the head is 0,A_(e)(j) for the chest is 1, and A_(e)(j) for the lower body part is 0.

FIG. 10 is a flowchart illustrating an exemplary process 1000 forselecting the imaging data according to some embodiments of the presentdisclosure. In some embodiments, the process 1000 may include omitting aportion of the imaging data before using the imaging data to reconstructan image.

In 1002, the processing module 134 may obtain motion amplitudes ofspatial points of a subject. In some embodiments, the motion amplitudesmay be determined according to the process 900. In some embodiments, themotion amplitudes may be determined using an external device. Forexample, an external device including a plurality of sensors may be usedto monitor the respiratory motion of the subject. The external devicemay generate a motion curve indicating the subject's movement during ascanning.

In 1004, the processing module 134 may determine a first motion periodand a second motion period based on the motion amplitudes of spatialpoints of the subject. In some embodiments, the first motion period mayrefer to a time period when the subject has the involuntary motion,while the second motion period may refer to a time period when thesubject has the voluntary motion. If the subject has a voluntary motionor the amplitude of a voluntary motion exceeds a threshold for a period,the imaging data collected during the period (e.g., a first motionperiod between a first motion phase and a second motion phase) may beomitted in image reconstruction to avoid or reduce motion artifact.

In some embodiments, the first motion period and the second motionperiod may be determined based on the motion curve determined by theexternal device. For example, the motion curve recording the amplitudeof motions of the subject's head for a time period. During a portion ofthe time period, the amplitude of motions of the subject's head exceedsa predetermined threshold. The portion of the time period may bedetermined as the second motion period, while the other portion of thetime period may be determined as the first motion period.

In some embodiments, the first motion period and the second motionperiod may be determined based on the motion amplitudes determined inprocess 900. For example, the imaging data may be collected duringseveral respiratory cycles. For each cycle, the processing module 134may determine motion amplitudes of the spatial points of the subject. Ifthe motion amplitudes of spatial points corresponding to a portion ofthe subject (e.g., the head, the feet, etc.) exceeds a voluntary motionthreshold, the subject may be considered to have a voluntary motion orthe amplitude of the voluntary motion exceeds the voluntary motionthreshold during the corresponding respiratory cycle. A respiratorycycle with the voluntary motion or a time period from the end period ofinspiration to the end period of expiration may be determined as thesecond motion period.

In 1006, the processing module 134 may omit imaging data collected inthe second motion period. As described before, the imaging datacollected in the second period may include voluntary motion data thatmay cause motion artifact in the image reconstruction. The imaging datacollected in the first motion period may be further used in imagereconstruction.

FIG. 12 is a flowchart illustrating an exemplary process 1200 forreconstructing a dual gated image according to some embodiments of thepresent disclosure.

In 1201, the processing module 134 may retrieve imaging data from ascanning of a subject, similar to the description in 902. The data maybe retrieved from the acquisition module 131 or a storage device (e.g.,the storage module 133, the storage unit 308, etc.).

In 1202, the processing module 134 may obtain a first motion signal of afirst motion type. In some embodiments, the first motion signal mayinclude motion information of the motion amplitudes, time, etc. of thefirst motion type of the spatial points of the subject. The first motiontype may include a type of voluntary motion as disclosed elsewhere inthe present disclosure. In some embodiments, a first motion signal ofthe first motion type may be obtained from a motion sensor used tocollect the motion information.

In 1203, the processing module 134 may obtain a second motion signal ofa second motion type. The second motion signal may include motioninformation the motion amplitudes, time, etc., of the second motion typeof the spatial points of the subject. The second motion type may includea type of involuntary motion. Exemplary involuntary motion includesrespiratory motion, cardiac motion, etc. In some embodiments, a secondmotion signal of the second motion type may be obtained using a sensorincluding, e.g., a sensor to measure the cardiac activities of asubject, a sensor to measure respiratory activities of subject, etc.

In 1204, the processing module 134 (e.g., the gating control unit 304 ofthe processing module 134) may determine first motion amplitudes of thefirst motion type for spatial points of the subject based on the firstmotion signal of the first motion type. In some embodiments, thedetermination may be implemented according to the process 900. Forexample, the processing module 134 may dividing the imaging data intogroups of the first gated imaging data based on the first motion signal.A group of the first gated imaging data may correspond to a motion phaseof the first motion type of the subject. For example, a first group ofthe first gated imaging data may correspond to a first motion phase ofthe first motion type. A second group of the first gated imaging datamay correspond to a second motion phase of the first motion type.

The reconstruction unit 306 may reconstructing a first gated imagecorresponding to the first motion phase of the first motion type usingthe first group of first gated imaging data and a second gated imagecorresponding to the second motion phase of the first motion type usingthe second group of first gated imaging data. In some embodiments, thereconstruction may be performed in a manner similar to the operationsdescribed in connection with 906.

The processing module 134 (e.g., the gating control unit 304 of theprocessing module 134) may register the first gated image and the secondgated image to determine a motion vector field of the first motion type.The motion vector field of the first motion type may include a pluralityof motion vectors of the first motion type. A motion vector of the firstmotion type may indicate a motion of the first motion type of a spatialpoint from the first motion phase to the second motion phase. Theregistration may be performed in a manner similar to the operationsdescribed in connection with 908.

The processing module 134 (e.g., the gating control unit 304 of theprocessing module 134) may determine a first motion amplitude of thefirst motion type for each spatial point based on the motion vectorfield of the first motion type (e.g., based on the motion vector of thefirst motion type corresponding to a spatial point). The determinationmay be performed in a manner similar to the operations described inconnection with 910.

In 1205, the processing module 134 (e.g., the gating control unit 304 ofthe processing module 134) may determine second motion amplitudes of thesecond motion type for spatial points of the subject based on the motionsignal of the second motion type. The determination may be implementedaccording to the process 900. For example, the processing module 134(e.g., the gating control unit 304 of the processing module 134) maydividing the imaging data based on the second motion signal. A group ofthe second gated imaging data may correspond to a motion phase of thesecond motion type of the subject. For example, a first group of thesecond gated imaging data may correspond to the end-inspiration phase ofa respiratory motion. A second group of the second gated imaging datamay correspond to the end-expiration phase of the respiratory motion. Asanother example, a first group of the second gated imaging data maycorrespond to the end-diastolic phase of a cardiac motion. A secondgroup of the second gated imaging data may correspond to theend-systolic phase of the cardiac motion.

The processing module 134 may execute similar operations in 1204,including the reconstructing, registering, and motion amplitudedetermination, with respect to the imaging data based on the secondmotion signal to obtain second motion amplitudes of the second motiontype of the spatial points.

The reconstruction unit 306 may reconstructing a first gated imagecorresponding to the first motion phase of the second motion type usingthe first group of second gated imaging data and a second gated imagecorresponding to the second motion phase of the second motion type usingthe second group of second gated imaging data. In some embodiments, thereconstruction may be performed in a manner similar to the operationsdescribed in connection with 906.

The processing module 134 (e.g., the gating control unit 304 of theprocessing module 134) may register the first gated image and the secondgated image to determine a motion vector field of the second motiontype. The motion vector field of the second motion type may include aplurality of motion vectors of the second motion type. A motion vectorof the second motion type may indicate a motion of a spatial point fromthe first motion phase to the second motion phase. The registration maybe performed in a manner similar to the operations described inconnection with 908.

The processing module 134 (e.g., the gating control unit 304 of theprocessing module 134) may determine a first motion amplitude of thesecond motion type for each spatial point based on the correspondingmotion vector field of the second motion type of the first motion type(e.g., based on the motion vector of the second motion typecorresponding to a spatial point). The determination may be performed ina manner similar to the operations described in connection with 910.

In 1206, the processing module 134 (e.g., the gating control unit 304 ofthe processing module 134) may divide or gate the imaging data based ondual gating of the first motion type and the second motion type. Thedual gating may be based on a first gating based on the motionamplitudes of spatial points of the first motion type and a secondgating based on the motion amplitudes of spatial points of the secondmotion type. The first gating or the second gating, or both, may beperformed based on a locally adaptive gating approach.

Under the first gating based on the motion amplitudes of spatial pointsof the first motion type, the imaging data are to be divided into Mframes; under the second gating based on the motion amplitudes ofspatial points of a second motion type, the imaging data are to bedivided into N frames. Under the dual gating, the imaging data aredivided to M×N frames.

For instance, from a period starting at 0, imaging data may be dividedunder the first gating into 5 frames, such that the imaging datacorresponding to a sub-period between 0-2 minutes belong to frame 1,imaging data corresponding to a sub-period between 2 minutes to 5minutes belong to frame 2, . . . , and a sub-period between 8 minutesand 10 minutes belong to frame 5. From a period starting at 0, imagingdata may be divided under the second gating into 10 frames, such thatthe imaging data corresponding to a sub-period between 0 and 0.5 minutesbelong to frame 1, and imaging data corresponding to a sub-periodbetween 0.5 minutes and 1 minutes belong to frame 2, . . . , and asub-period between 9.5 minutes and 10 minutes belong to frame 10. Underthe dual gating, the imaging data are divided into 50 frames. Thelengths of the sub-periods may be the same or different.

The imaging data within a frame corresponding to a spatial point may beassociated with a combined motion amplitude relating to both the firstmotion type and the second motion type. For a spatial point, thecombined motion amplitude may be determined based on the first motionamplitude and the second motion amplitude of the spatial point. Thefirst motion type or the second motion type may be a voluntary motion oran involuntary motion. An involuntary motion may include, e.g.,respiratory motion, cardiac motion, etc.

In 1207, for each spatial point, the processing module 134 (e.g., thegating control unit 304 of the processing module 134) may assess atemporal spread function based on the combined motion amplitude of thespatial point. In some embodiments, the temporal spread function of aspatial point may be assessed further based on a first resolutionrecovery of the first motion type, and a second resolution recovery ofthe second motion type, in addition to the combined motion amplitude.For instance, the temporal spread function of a spatial point may bedetermined according to any one of equations (9)-(11) describedelsewhere in the present disclosure. A resolution recovery, e.g. thefirst resolution recovery, the second resolution recovery, etc., may bedetermined based on the intrinsic system resolution. It may also bedefined by a user. For instance, a user may specify a desired targetresolution which may be larger than the intrinsic system resolution. Aresolution recovery is larger than or equal to the intrinsic systemresolution.

In 1208, the reconstruction unit 306 may reconstruct a dual gated imagefrom the imaging data gated based on dual locally adaptive gating andthe temporal spread functions. For instance, the image reconstructionmay be performed according to equation (7) described elsewhere in thepresent disclosure.

In some embodiments, the processing module 134 (e.g., the gating controlunit 304 of the processing module 134) may determine an intra-framemotion amplitude based on an intrinsic system resolution, which in turndepends on the material of the detector 112. As used herein, anintra-frame motion amplitude may refer to the residue motion aftermotion. The intra-frame motion amplitude determines the motion blurringeffects. When the intra-frame motion amplitude is smaller than that ofthe intrinsic system resolution, no significant motion blurring wouldoccur. For instance, the processing module 134 may further determine thenumber of gate corresponding to the second motion signal according tolocally adaptive gating based on the intra-frame motion amplitude.

In some embodiments, the processing module 134 (e.g., the gating controlunit 304 of the processing module 134) may further determine a gatingarea. In some embodiments, to perform the first gating, a first gatingarea may be selected. Similarly, to perform the second gating, a secondgating area may be selected. The selection of the first gating area maybe performed independently from the selection of the second gating area.The selection of the first gating area and/or the second gating area maybe performed based on a user input (e.g., as illustrated in FIG. 11), orby the system 100. For instance, the selection of the first gating areaand/or the second gating area may be performed by the system 100 basedon the comparison between the motion amplitudes of spatial points of thevoluntary motion with a threshold.

Merely by way of example, the processing module 134 may compare secondmotion amplitudes of the second motion type of the spatial points with athreshold that relates to a system intrinsic resolution. The processingmodule 134 may determine the gating area based on the comparison.

EXAMPLES

The following examples are provided for illustration purposes, and notintended to limit the scope of the present disclosure.

Example 1

FIG. 7-A, FIG. 7-B, and FIG. 7-C illustrate exemplary ECT imagesregarding a portion of a patient generated by different reconstructionapproaches according to some embodiments of the present disclosure. Forillustration purposes, 2D images are shown. The ECT image illustrated inFIG. 7-A was reconstructed based on the projection data that wasprocessed based on the temporal spread function described in thisdisclosure. The ECT image illustrated in FIG. 7-B was reconstructedbased on a non-gating approach (e.g., a point spread function approach).The ECT image illustrated in FIG. 7-C was reconstructed based on theprojection data that was gated according to a uniform gate number.

It may be seen that the ECT image in FIG. 7-B has a low noise level butpoor image resolution. The ECT image in FIG. 7-C has a high imageresolution but high noise level. The ECT image in FIG. 7-A has a highnoise level and a high image resolution. The noise level of the ECTimage illustrated in FIG. 7-A is similar to that in FIG. 7-B. The imageresolution of the ECT image illustrated in FIG. 7-A is similar to thatin FIG. 7-C.

Example 2

FIG. 8-A, FIG. 8-B, and FIG. 8-C illustrate exemplary ECT imagesregarding phantom generated by different reconstruction approachesaccording to some embodiments of the present disclosure. Forillustration purposes, 2D images are shown. The ECT image illustrated inFIG. 8-A was reconstructed based on the projection data that wasprocessed based on the temporal spread function described in thisdisclosure. The ECT image illustrated in FIG. 8-B was reconstructedbased on a non-gating approach (e.g., a point spread function method).The ECT image illustrated in FIG. 8-C was reconstructed based on theprojection data that was gated according to a uniform gate number. Itmay be seen that the ECT image in FIG. 8-B has a low noise level butpoor image resolution. The ECT image in FIG. 8-C has a high imageresolution but high noise level. The ECT image in FIG. 8-A has a lownoise level and a high image resolution. The noise level of the ECTimage illustrated in FIG. 8-A is similar to that in FIG. 8-B. The imageresolution of the ECT image illustrated in FIG. 8-A is similar to thatin FIG. 8-C.

Example 3

FIG. 11 illustrates an exemplary user interface for manually selecting agating area according to some embodiments of the present disclosure. Asshown in the figure, three reconstructed images were arranged on theuser interface. The image A is an image obtained through a scoutreconstruction to provide a scout image. A gating area was determinedbased on an instruction provided by a user via a user interface, and thecorresponding imaging data within the gating area were gated and appliedin image reconstruction to generate image C. The image B is an imagereconstructed without a gating approach. The image C is an image with alocally adaptive gating approach. According to the locally adaptivegating approach, a spatially variant gating number was applied locallyto the imaging data for reconstruction. In the image A, a circle 1101was determined by a user using the tools (the icons on the userinterface) on the user interface. For example, an area selection toolmay be used to select a circular area on the image of the subject todetermine an area to be gated. The imaging data corresponding to spatialpoints included in the circle were gated during the reconstruction.Other exemplary icons may include an icon for rotating an image, an iconfor generating a symmetric image, icons for zoom in/out, an icon foradjusting brightness, an icon for adjusting contrast, or the like, or acombination thereof.

It may be seen that image C in FIG. 11 has a high image resolution thanthat of the image B in FIG. 11.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method for image processing implemented on at least onemachine each of which has at least one processor and at least onestorage device, the method comprising: a) obtaining imaging dataacquired from a scanning of a subject; b) obtaining a first motionsignal of a first motion type; c) obtaining a second motion signal of asecond motion type; d) dividing, based on the first motion signal, theimaging data into a plurality of groups of first gated imaging data,wherein a group of the first gated imaging data corresponds to arespective motion phase of the first motion type of the subject, a firstgroup of the first gated imaging data corresponding to a first motionphase of the first motion type of spatial points of the subject, and asecond group of the first gated imaging data corresponding to a secondmotion phase of the first motion type of the spatial points of thesubject; e) reconstructing a first gated image corresponding to thefirst motion phase of the first motion type using the first group of thefirst gated imaging data and a second gated image corresponding to thesecond motion phase of the first motion type using the second group ofthe first gated imaging data; f) determining a motion vector field ofthe first motion type by registering the first gated image and thesecond gated image, wherein the motion vector field of the first motiontype includes a plurality of motion vectors of the first motion type, amotion vector of the first motion type indicating a motion of arespective spatial point of the first motion type from the first motionphase to the second motion phase; g) dividing, based on the secondmotion signal, the imaging data into a plurality of groups of secondgated imaging data, wherein a group of the second gated imaging datacorresponds to a respective motion phase of the second motion type ofthe subject, a first group of the second gated imaging datacorresponding to a first motion phase of the second motion type of thespatial points of the subject, and a second group of the second gatedimaging data corresponding to a second motion phase of the second motiontype of the spatial points of the subject; h) obtaining a motion vectorfield of the second motion type by performing e) and f) with respect tothe imaging data divided based on the second motion signal; i) dualgating, based on the first motion signal and the second motion signal,the imaging data; j) for each of the spatial points, assessing atemporal spread function based on the motion vector of the first motiontype and the motion vector of the second motion type of the spatialpoint; and k) reconstructing an image from the dual gated imaging dataand the temporal spread functions.
 2. The method of claim 1, wherein thefirst motion type corresponds to a voluntary motion, and the secondmotion type corresponds to an involuntary motion.
 3. The method of claim1, wherein the determining the motion vector field of the first motiontype by registering the first gated image and the second gated imageincludes: performing a 2D image registration of the first gated imageand the second gated image; determining, for each of the spatial points,a 2D motion vector in a coronal plane and a 2D motion vector in asagittal plane based on the registration; and determining the motionvector field based on 2D motion vectors corresponding to the spatialpoints in the coronal plane and 2D motion vectors corresponding to thespatial points in the sagittal plane, wherein the motion vector field isthree-dimensional.
 4. The method of claim 1, wherein the registering thefirst gated image and the second gated image includes performing a 3Dimage registration, the method further comprising: determining themotion vector field based on the 3D image registration, wherein themotion vector field is three-dimensional.
 5. The method of claim 1,further comprising: for each of the spatial points, determining a firstmotion amplitude based on the corresponding motion vector of the firstmotion type; and determining a second motion amplitude based on thecorresponding motion vector of the second motion type, wherein thetemporal spread function is accessed based on the first motion amplitudeand the second motion amplitude.
 6. The method of claim 1, wherein thedual gating, based on the first motion signal and the second motionsignal, the imaging data comprises: dual gating, based on the firstmotion signal and the second motion signal, the imaging data based on alocally adaptive gating approach.
 7. The method of claim 6, wherein thereconstructing an image from the dual gated imaging data and thetemporal spread functions comprises: determining an intra-frame motionamplitude based on a system intrinsic resolution; and determining a gatenumber corresponding to the second motion signal according to thelocally adaptive gating approach based on the intra-frame motionamplitude.
 8. The method of claim 1, wherein the reconstructing an imagefrom the dual gated imaging data and the temporal spread functionscomprises: determining a gating area corresponding to the first motionsignal or the second motion signal; determining a plurality of gatenumbers for a plurality of voxels corresponding to spatial pointsrepresented in the gating area; and determining the correspondingtemporal spread functions for the plurality of voxels based on theplurality of gate numbers and the motion vectors of the spatial pointscorresponding to the first motion signal or the second motion signal. 9.The method of claim 8, wherein the determining the gating areacomprises: comparing the second motion amplitudes of the second motiontype of the spatial points with a first threshold that relates to asystem intrinsic resolution; and determining the gating area based onthe comparison.
 10. A system for image processing, comprising: a storagedevice storing instructions; and a processor in communication with thestorage, wherein when executing the instructions, the processor iscaused to perform operations including: a) obtaining imaging dataacquired from a scanning of a subject; b) obtaining a first motionsignal of a first motion type; c) obtaining a second motion signal of asecond motion type; d) dividing, based on the first motion signal, theimaging data into a plurality of groups of first gated imaging data,wherein a group of the first gated imaging data corresponds to arespective motion phase of the first motion type of the subject, a firstgroup of the first gated imaging data corresponding to a first motionphase of the first motion type of spatial points of the subject and asecond group of the first gated imaging data corresponding to a secondmotion phase of the first motion type of the spatial points of thesubject; e) reconstructing a first gated image corresponding to thefirst motion phase of the first motion type using the first group of thefirst gated imaging data and a second gated image corresponding to thesecond motion phase of the first motion type using the second group ofthe first gated imaging data; f) determining a motion vector field ofthe first motion type by registering the first gated image and thesecond gated image, wherein the motion vector field of the first motiontype includes a plurality of motion vectors of the first motion type, amotion vector of the first motion type indicating a motion of arespective spatial point of the first motion type from the first motionphase to the second motion phase; g) dividing, based on the secondmotion signal, the imaging data into a plurality of groups of secondgated imaging data, wherein a group of the second gated imaging datacorresponds to a respective motion phase of the second motion type ofthe subject, a first group of the second gated imaging datacorresponding to a first motion phase of the second motion type of thespatial points of the subject, and a second group of the second gatedimaging data corresponding to a second motion phase of the second motiontype of the spatial points of the subject; h) obtaining a motion vectorfield of the second motion type by performing e) and f) with respect tothe imaging data divided based on the second motion signal; i) dualgating, based on the first motion signal and the second motion signal,the imaging data; j) for each of the spatial points, assessing atemporal spread function based on the motion vector of the first motiontype and the motion vector of the second motion type of the spatialpoint; and k) reconstructing an image from the dual gated imaging dataand the temporal spread functions.
 11. The system of claim 10, whereinthe first motion type corresponds to a voluntary motion, and the secondmotion type corresponds to an involuntary motion.
 12. The system ofclaim 10, wherein the determining the motion vector field of the firstmotion type by registering the first gated image and the second gatedimage includes: performing a 2D image registration of the first gatedimage and the second gated image; determining, for each of the spatialpoints, a 2D motion vector in a coronal plane and a 2D motion vector ina sagittal plane based on the registration; and determining the motionvector field based on 2D motion vectors corresponding to the spatialpoints in the coronal plane and 2D motion vectors corresponding to thespatial points in the sagittal plane, wherein the motion vector field isthree-dimensional.
 13. The system of claim 10, wherein the registeringthe first gated image and the second gated image includes performing a3D image registration, the method further comprising: determining themotion vector field based on the 3D image registration, wherein themotion vector field is three-dimensional.
 14. The system of claim 10,the operations further including: for each of the spatial points,determining a first motion amplitude based on the corresponding motionvector of the first motion type; and determining a second motionamplitude based on the corresponding motion vector of the second motiontype, wherein the temporal spread function is accessed based on thefirst motion amplitude and the second motion amplitude.
 15. The systemof claim 10, wherein the dual gating, based on the first motion signaland the second motion signal, the imaging data comprises: dual gating,based on the first motion signal and the second motion signal, theimaging data based on a locally adaptive gating approach.
 16. The systemof claim 15, wherein the reconstructing an image from the dual gatedimaging data and the temporal spread functions comprises: determining anintra-frame motion amplitude based on a system intrinsic resolution; anddetermining a gate number corresponding to the second motion signalaccording to the locally adaptive gating approach based on theintra-frame motion amplitude.
 17. The system of claim 10, wherein thereconstructing an image from the dual gated imaging data and thetemporal spread functions comprises: determining a gating areacorresponding to the first motion signal or the second motion signal;determining a plurality of gate numbers for a plurality of voxelscorresponding to spatial points represented in the gating area; anddetermining the corresponding temporal spread functions for theplurality of voxels based on the plurality of gate numbers and themotion vectors of the spatial points corresponding to the first motionsignal or the second motion signal.
 18. The system of claim 17, whereinthe determining the gating area comprises: comparing the second motionamplitudes of the second motion type of the spatial points with a firstthreshold that relates to a system intrinsic resolution; and determiningthe gating area based on the comparison.
 19. A non-transitory computerreadable medium, comprising executable instructions that, when executedby at least one processor, direct the at least one processor to performa method, the method comprising: a) obtaining imaging data acquired froma scanning of a subject; b) obtaining a first motion signal of a firstmotion type; c) obtaining a second motion signal of a second motiontype; d) dividing, based on the first motion signal, the imaging datainto a plurality of groups of first gated imaging data, wherein a groupof the first gated imaging data corresponds to a respective motion phaseof the first motion type of the subject, a first group of the firstgated imaging data corresponding to a first motion phase of the firstmotion type of spatial points of the subject and a second group of thefirst gated imaging data corresponding to a second motion phase of thefirst motion type of the spatial points of the subject; e)reconstructing a first gated image corresponding to the first motionphase of the first motion type using the first group of the first gatedimaging data and a second gated image corresponding to the second motionphase of the first motion type using the second group of the first gatedimaging data; f) determining a motion vector field of the first motiontype by registering the first gated image and the second gated image,wherein the motion vector field of the first motion type includes aplurality of motion vectors of the first motion type, a motion vector ofthe first motion type indicating a motion of a respective spatial pointof the first motion type from the first motion phase to the secondmotion phase; g) dividing, based on the second motion signal, theimaging data into a plurality of groups of second gated imaging data,wherein a group of the second gated imaging data corresponds to arespective motion phase of the second motion type of the subject, afirst group of the second gated imaging data corresponding to a firstmotion phase of the second motion type of the spatial points of thesubject and a second group of the second gated imaging datacorresponding to a second motion phase of the second motion type of thespatial points of the subject; h) obtaining a motion vector field of thesecond motion type by performing e) and f) with respect to the imagingdata divided based on the second motion signal; i) dual gating, based onthe first motion signal and the second motion signal, the imaging data;j) for each of the spatial points, assessing a temporal spread functionbased on the motion vector of the first motion type and the motionvector of the second motion type of the spatial point; and k)reconstructing an image from the dual gated imaging data and thetemporal spread functions.
 20. The non-transitory computer readablemedium of claim 19, wherein the first motion type corresponds to avoluntary motion, and the second motion type corresponds to aninvoluntary motion.